Background

With more than 940,000 new colorectal cancer cases worldwide each year, there
is no better way for colorectal cancer routine screening. The aim of this study
was to investigate whether the fatty acid binding to albumin is detectably and
significantly altered in colorectal cancer patients when compared with healthy
people, in order to find a better way for colorectal cancer diagnosis.

Methods

One hundred and forty-one patients operatively treated for colorectal cancer
were included in the examination, and 180 healthy people were also enrolled as
controls. Commercial 16-doxyl stearic acid was used as spin probe. Serum albumin
was analyzed by electron paramagnetic resonance (EPR) with spin probe.
Discriminant analysis was carried out using the measured EPR spectra by SPSS
20.0.

Results

Of the original grouped cases, 89.4% were correctly classified. Of the
cross-validated grouped cases, 86.9% were correctly classified. Using Fisher
linear discriminant analysis we were able to develop a mathematical model allowing
for identification of colorectal cancer patients based on five values (both
relative intensity and peak width) which are obtained from the EPR
spectrum.

Conclusions

Cancer-associated alterations to albumin can be assessed by spin-label EPR.
The potential applications for this diagnostic technique are significant and
represent a cost-effective means for screening patients with cancer. Spin probe
for diagnosis of colorectal cancer might be a useful tool and further studies
should take place in order to investigate all stages of colorectal cancer
patients.

Colorectal cancer is the second most common cancer and the second most common
cause of death by cancer [1]. The
clinical stage of the disease at diagnosis often determines the prognosis and
survival rate of a patient with colorectal cancer [2]. If the colorectal cancer patient could be diagnosed at an early
stage, they will have a better treatment than if diagnosed at an advanced stage.
However, insufficient evidence concerning prognostic and predictive value exists for
other molecular factors such as thymidylate synthase, microsatellite instability
(MSI), p-53 and K-ras. [3].

Human serum albumin (HSA) is the main component for transport of a variety of
peptides and of water-insoluble fatty acids (FA) in the serum [4]. Its remarkable ability for binding FA has
motivated our group to use spin-labeled derivatives of stearic acids to monitor
conformational changes around its binding sites [5]. Seven long-chain FA binding sites have been described so far
[6]. Proteins released from tumor
cells are able to bind to albumin and thus lead to a modification of its structure
and function [6, 7]. As a consequence, the binding and transport
capacities for FA are also changed. These changes can be detected by electron
paramagnetic resonance (EPR)/electron spin resonance spectroscopy [8, 9].
EPR is suitable for the determination of functional characteristics of plasma
proteins [7, 10]. EPR spectroscopy detects radicals that are,
in the case of HSA, introduced artificially using long-chain FA that have a stable
radical (doxyl) chemically attached and by analyzing how these spin-labeled FA bind
to albumin [7]. Use of this technique
with 16-doxyl stearic acid (2-(14-carboxytetradecyl)-2-ethyl-4,
4-dimethyl-3-oxazolidinyloxy) as spin probe has previously demonstrated
cancer-specific alterations in albumin conformation [10, 11]. We used EPR
spectroscopy to investigate the diagnostic utility of serum albumin conformation
analysis in patients with colorectal cancer and chronic disease.

Samples

All patients who presented to department of surgical oncology PLA People's
Liberation Army General Hospital had been pathologically diagnosed with colorectal
cancer during the study period. Samples were collected from 141 patients with
colorectal cancer and 180 blood donors and other volunteers known to be in good
health. All patients provided their consent for participation in the study
(approved by Institute of Radiation Medicine Chinese Academic of Medical Sciences
ethics committee). Table 1 shows the
detailed information. Blood was obtained by standard venipuncture techniques and
collected without any additive. After clotting, serum was separated by
centrifugation for 10 minutes, isolated, and then stored at −20 °C before
analysis.

Sample preparation

Commercial 16-doxyl stearic acid (Sigma-Aldrich GmbH, Munich, Germany) was
used as spin probe. This compound was chosen because of the extremely high binding
constant of albumin for stearic acid (6.9 × 107 L/mol),
generally leading to 99.9% binding of this spin probe to albumin. Here we used a
defined concentration of spin probe-pure ethanol compounds to perform the
experiment. Each aliquot received a defined concentration of spin probe-pure
ethanol compounds with 50 μL of serum and then transformed to a microliter shaker
for 10 minutes at 25°C covered by parafilm. The aliquots were then transferred
into capillary glass tubes for analysis within the EPR spectrometer. Each sample
was measured three times.

Sample measurement

We measured the EPR spectra of each sample with a commercially available EPR
spectrometer (Bruker EMX A300, Ettlingen, Germany). The spectrometer operating
conditions adopted during the experiments are given in Table 2.

Table 2

The spectrometer operating conditions adopted during
the experiment

Central field

3515 G

Sweep width

150 G

Microwave frequency

9.864 GHz

Microwave power

15.94 MW

Modulation frequency

100 KHz

Modulation amplitude

10 G

Receiver gain

20

Sweep time

20 s

Time constant

0.16 ms

As the EPR spectrum is comprised of 1024 data points, we just used Matlab
(version 7.0 Math Works Natick, Massachusetts, U.S.A.) to make up a small program
to simulate the spectrum curve using least-square fitting and calculated the peak
width and the relative intensity.

Statistical analysis

Gammerman and colleagues [12] and
de Noo and colleagues [13] described
a double cross-validatory implementation of linear discriminant analysis for the
calibration of a diagnostic rule based on a single spectrum per patient (and for a
single fractionation). Due to non-normal distribution of the raw data, a
logarithmic transformation was needed. To permit comparisons with other studies,
results are presented as means with the standard deviation obtained after the Ln
transformation. Comparisons or correlations were evaluated by using non-parametric
tests (Kruskall Wallis one-way variance analysis or Spearman Rank test,
respectively) on the raw data, and by means of Student’s t test on normalized data. Discriminant analysis was carried out
using the measured EPR spectra by SPSS 20.0 (IBM, Armonk, New York, U.S.A).
Selected values for variables such as relative intensity and peak widths can then
be used to estimate the biophysical characteristics of the 16-doxyl stearic acid
spin label. The selected parameters are shown in Figure 1. The analysis was performed using the option of the 'equal
prior probability' to assign the subjects to groups.

Figure 1

Selected values in a typical
spectrum. ΔH1ΔH2ΔH3 show the relative intensity; ΔH4ΔH5 show
the relative peak widths.

All the predictor variables were subjected to stepwise discriminant function
analysis, which has the potential to optimally separate the two groups;
furthermore, the statistical significance was assessed using Wilks’ lambda. The
variables having the higher discriminant function coefficient were included in the
discriminant function for developing the formula.

F=di1V1+di2V2+diPVP+C

Where F is the discriminant function score, di is the
discriminant function coefficient, V is the score of the predictor variable and C
is the discriminant function constant.

As shown in Table 3, the five selected
values (shown in Figure 1) were
statistically different between the two groups. The P values of the five parameters were all less than 0.001, which means
they are significantly different between groups. Linear discriminant analysis was
used based on the five selected parameters.

Table 3

Medians and standard deviation of the variables
analyzed and means and standard deviations of Ln-transformed levels of
selected values

b) is the data after Ln-transformation. Ln is Logarithmic
transformation. ΔH1 is changed into LnΔH1 after Ln Logarithmic
transformation.

Table 4 shows the accuracy of the
discriminant function coefficient for all the predictor variables which were
included in the study, from which the highest accurate values were included for the
generation of discriminant function. The discriminant analysis produced the best
discriminant functions and the predictor variables included in the functions were
ΔH1ΔH2ΔH3ΔH4 and ΔH5 based on the greatest univariate discriminant coefficient.
Before the formula was calculated with the greatest univariate discriminant
coefficient, they were subjected to a test of significance using Wilks’ lambda. It
was found the entire assigned predictor variables showed statistical significance at
P < 0.05 (Table 4).

The value obtained using discriminant function for cancer patients and healthy
people is calculated, respectively. This shows that this discriminant function
formula can accurately identify cancer in this population. To access whether it is
possible to generate accurate cancer diagnosis models from data collected for this
study, discriminant functions were calculated and tested using cross-validation.
This was performed using SPSS, and the leave-one-out method was chosen to calculate
the cross-validation error rate (Table 5).
The discriminant function used in the present study describes the optimal separation
between the patients and healthy controls, and also shows that there are significant
differences between them. This is substantiated by the classification accuracy of
functions provided in Table 5. Hence, the
original grouped cases correctly classified were 89.4%.

Table 5

Classification accuracy checked using cross-validation
for the developed discriminant function

Classification
resultsa,b

Group

Predicted group membership

Total

1

2

Original

Count

1

128

13

141

2

21

159

180

%

1

90.8

9.2

100.0

2

11.7

88.3

100.0

Cross-validatedc

Count

1

124

17

141

2

25

155

180

%

1

87.9

12.1

100.0

2

13.9

86.1

100.0

a89.4% of original grouped cases were
correctly classified; b86.9% of cross-validated
grouped cases correctly classified;
ccross-validation is done only for those cases in
the analysis. In cross-validation, each case is classified by the functions
derived from all cases other than that case.

Albumin is the single most abundant protein in nonpathogenic plasma, comprising
approximately two-thirds of total plasma proteins [4, 9, 11]. This study shows that the ability of albumin
to bind FA is significantly altered in patients with colorectal cancer. This
modification is likely caused by the presence of bioactive peptides and other
substances from tumor tissue [5,
12, 14]. The shape of the EPR spectrum reflects the state of the spin
probe molecules, such as characteristics of its molecular motion and electrical and
magnetic fields in the surrounding environment [11, 15, 16]. Results of recent application of EPR
spectroscopy in animal models and humans suggest that EPR has great diagnostic
potential [9–11].

Discriminant functions have become a widely used method for disease
discrimination [12]. Stepwise
discriminant function analysis was applied which calculates the optimum combination
of variables for discriminant function and weighs them to reflect their contribution
to the determination [12, 13]. A deficiency of the current study was the
significant difference in physical conditions between healthy individuals and
patients with colorectal cancer. Postoperative patients only represent part of the
patients with colorectal cancer. The effect of chemotherapy, which might have
significant influence on tumor-related metabolite binding to albumin [15, 17], as well as EPR spectral differences caused by tumor stage and
localization [18, 19], have not been analyzed here. Automation of
the pipetting and dilution steps would also probably enhance the precision of the
procedure [11].

The obtained results show that cancer-associated alterations to albumin can be
assessed by spin-label EPR [9,
11]. Using Fisher linear discriminant
analysis we were able to develop a mathematical model allowing for identification of
colorectal cancer patients with an 89.4% success rate based on fives values (of both
relative intensity and peak width) which are obtained from the EPR spectrum. The
potential applications for this diagnostic technique are significant and represent a
cost-effective means for screening patients with cancer [11, 19]. Further studies should take place in order to investigate all
stages of colorectal cancer patients.

Statement

The study was approved by the local ethical committee and all individuals
provided written informed consent for study participation.

This article is published under license to BioMed Central Ltd. This is
an Open Access article distributed under the terms of the Creative Commons
Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in
any medium, provided the original work is properly cited.